Identification of blood-based transcriptomics biomarkers for Alzheimer's disease using statistical and machine learning classifier

Abdullah, Mohammad; Wah, Yap Bee; Majeed, Abu Bakar Abdul; Zakaria, Yuslina; Shaadan, Norshahida

doi:10.1016/j.imu.2022.101083

Cited by 7 publications

(4 citation statements)

References 46 publications

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“…A study by Abdullah et al. aimed to find the transcriptomics candidate biomarkers that could most accurately classify AD patients in Malaysia ( 122 ). They used Boruta’s feature selection algorithm on a transcriptomics dataset from the TUA study, comprising 22.254 transcript genes of 92 AD patients and 92 HC.…”

Section: Omics-based Biomarker Signature Discovery and Machine Learningmentioning

confidence: 99%

Towards early diagnosis of Alzheimer’s disease: advances in immune-related blood biomarkers and computational approaches

Krix,

Wilczynski,

Falgàs

et al. 2024

Front. Immunol.

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Alzheimer’s disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer’s disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer’s disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer’s disease.

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Section: Omics-based Biomarker Signature Discovery and Machine Learningmentioning

confidence: 99%

Towards early diagnosis of Alzheimer’s disease: advances in immune-related blood biomarkers and computational approaches

Krix,

Wilczynski,

Falgàs

et al. 2024

Front. Immunol.

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“…The selection of whole blood tissue was due to three reasons: i) a large sample size exists (n=574) for the whole blood tissue in the reference GTEx V8 dataset; ii) gene expression in the whole blood and that of the brain's cortex were found correlated [17]; iii) recent studies have demonstrated that gene expressions in whole blood could serve as biomarkers for AD dementia [18,19]. For example, multiple studies showed that blood-based transcriptomics biomarkers predict AD risks, such as in Korukonda [22],and Lee et. al Scientific Reports 2020 [23].…”

Section: Introductionmentioning

confidence: 99%

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia

Guo,

Yang

2024

Alz Res Therapy

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Background Transcriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia. Methods We first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene. Results We identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD. Conclusion We applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.

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“…Several studies have been conducted in recent times regarding the etiology of AD, with the majority of the research indicating that the cause of AD was genetic in nature [12]. Despite the increasing number of genes that have been suggested to impact vulnerability to Alzheimer's disease (AD), the comprehension of their mechanisms and the enhancement of disease management are still restricted by challenges in understanding the functional implications of genetic associations [13], [14].…”

Section: Introductionmentioning

confidence: 99%

A meta-analysis of bulk RNA-seq datasets identifies potential biomarkers and repurposable therapeutics against Alzheimer’s disease

Lamisa,

Ahammad,

Bhattacharjee

et al. 2023

Preprint

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Alzheimer's disease (AD) poses a major challenge due to its impact on the elderly population and the lack of effective early diagnosis and treatment options. In an effort to address this issue, a study focused on identifying potential biomarkers and therapeutic agents for AD was carried out. Using RNA-Seq data from AD patients and healthy individuals, 12 differentially expressed genes (DEGs) were identified, with 9 expressing upregulation (ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX, ST8SIA2, NNAT, and PCDH11Y) and 3 expressing downregulation (LTF, XIST, and TTR). Among them, TTR exhibited the lowest gene expression profile. Interestingly, functional analysis tied TTR to amyloid fiber formation and neutrophil degranulation through enrichment analysis. These findings suggested the potential of TTR as a diagnostic biomarker for AD. Additionally, druggability analysis revealed that the FDA-approved drug Levothyroxine might be effective against the Transthyretin protein encoded by the TTR gene. Molecular docking and dynamics simulation studies of Levothyroxine and Transthyretin suggested that this drug could be repurposed to treat AD. However, additional studies using in vitro and in vivo models are necessary before these findings can be applied in clinical applications.

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Identification of blood-based transcriptomics biomarkers for Alzheimer's disease using statistical and machine learning classifier

Cited by 7 publications

References 46 publications

Towards early diagnosis of Alzheimer’s disease: advances in immune-related blood biomarkers and computational approaches

Towards early diagnosis of Alzheimer’s disease: advances in immune-related blood biomarkers and computational approaches

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia

A meta-analysis of bulk RNA-seq datasets identifies potential biomarkers and repurposable therapeutics against Alzheimer’s disease

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